Univariate summary plots
Nick Toimieri
sys.date()
1 NOTES
Prior to running this file, run:
- _01_swath-fish-update.R
- _02_swath-inverts-update.R
to update the data for the most recent year. You will also need to have:
- swath-process-pre-2017-data.R
- R-functions-ocnms.R
as helper files.
2 DATA YEAR
Set the data year year
rm(list = ls())
data_year = 20242.1 some prep info
3 Process raw data
- Copy the xlxm files into the year folder.
- Open and save as xlsx files.
3.1 Bring in data and species codes
## [1] "Loading fish"
## [1] "Running fish"## [1] "Finished fish"
## [1] "Loading inverts & algae"
## [1] "Running inverts & algae"
## [1] "Running pre-2015 file"## [1] "ACAHUD"
## [1] "ACANAN"
## [1] "ACMMIT"
## [1] "AGAFIM"
## [1] "ALAMAR"
## [1] "ANTELE"
## [1] "ANTXAN"## [1] "BALNUB"
## [1] "CADLUT"
## [1] "CANORE"
## [1] "CANSPP"
## [1] "CERFOL"## [1] "CHITON"## [1] "CLAMSP"## [1] "COSCOS"## [1] "CRAARB"
## [1] "CRAGIG"## [1] "CROPAP"
## [1] "CRYDEC"## [1] "CRYSIT"
## [1] "CRYSTE"## [1] "CUCMIN"## [1] "CYMTRI"## [1] "DERIMB"## [1] "DESSPP"## [1] "DIASAN"
## [1] "DIOASP"## [1] "DIRALB"## [1] "DORODH"## [1] "dPTECAL"
## [1] "EGRMEN"
## [1] "ENTDOF"
## [1] "EPIPRO"## [1] "EUPQUI"## [1] "EVATRO"## [1] "FUSORE"## [1] "HALKAM"
## [1] "HENLEV"## [1] "HERCRA"
## [1] "JANFUS"
## [1] "LAMSET"## [1] "LEPSPP"
## [1] "LIMCOC"
## [1] "LIRDIR"
## [1] "LOPMAN"
## [1] "MACPYR"## [1] "MEDAEQ"## [1] "MESFRA"
## [1] "METGIG"
## [1] "METSPP"
## [1] "MIMFOL"## [1] "MYTCAL"
## [1] "NERLUE"## [1] "NO_ALG"
## [1] "NUCLAM"
## [1] "NUDIBR"## [1] "ORTKOE"
## [1] "PAGSPP"
## [1] "PARCAL"## [1] "PATMIN"
## [1] "PELNOB"## [1] "PISBRE"
## [1] "PISGIG"
## [1] "PISOCH"## [1] "PLEGAR"## [1] "PODSPP"## [1] "POLSPP"
## [1] "PTECAL"## [1] "PTETES"
## [1] "PUGGRA"
## [1] "PUGPRO"## [1] "PYCHEL"## [1] "SACGRO"## [1] "SACLAT"
## [1] "SCALLOP"
## [1] "SCYDEC"## [1] "SOLDAW"
## [1] "SOLSTI"## [1] "STARREC"## [1] "STRDRO"## [1] "STRPUR"## [1] "STYFOR"
## [1] "STYMON"## [1] "TEGSPP"
## [1] "TRICAT"## [1] "UNIDSP"
## [1] "URTCRA"## [1] "URTLOF"## [1] "URTPIS"
## [1] "URTSPP"4 Data import
Currently the species codes are in separate files from the data. Need to change above xlsx files to contain additional columns for the code sheets.
5 Triggers and Settings
I set some common settings here. The settings.rds file ports the same info over to the multivariate rmd. It is brought in and updated here.
5.1 Set a common theme for plots
6 Manipulate fish data prior to plotting.
Raw data are fish counts by size, so there are multiple observations per species per transect. Sum up counts by transect.
7 Some basic information for summing and analyses below
Minimum vis for fish analysis = 2-m
Kelp depths = 5 -m Fish depths = 5, 10 -m Inv depths = 5 -m
Visibility data for 2015 are fake. DI was bad and set at 1.0 m; other sites set at 3.0 m
8 FISH
8.1 Rockfish YOY - coastwide
Mean abundance of YOY rockfish, averaged across all sites.
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
## Joining with `by = join_by(taxa)`
8.2 Adult fish - coastwide
Species mean abundance across sites for depths listed below.
Fish depth = 5, 10
## Joining with `by = join_by(taxa)`
8.3 Adult Fish - site x year x depth
Species mean abundance by site x year x depth
## Joining with `by = join_by(taxa)`
## YOY rockfish abundance - site x year x depth
Species mean abundance by site x year x depth
## Joining with `by = join_by(taxa)`
# ALGAE & INVERT DATA
Summarize kelp and invert data to get count by transect. Raw files has more detailed observations. Data are imported above.
Kelp depth = 5
A bunch more summarizing at different scales. Also output for multivariate analyses.
## `summarise()` has grouped output by 'year', 'site', 'transect', 'species',
## 'zone', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'species', 'zone', 'area'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'species', 'zone'. You can
## override using the `.groups` argument.
## Joining with `by = join_by(site)`9 ALGAE
9.1 Nereocystis at Tatoosh
Not sure why I started with this, but the figure is Nereocystis stipe counts at Tatoosh Island.
9.2 Summed Nerocycstis and Macrocystis stipes m2: year x site x depth
Essentially total canopy kelp stipe counts at all sites. Bit odd and not used because of difference in Macro and Nero stipes.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site'. You can override using the
## `.groups` argument.
9.3 Stipe counts for Nerocystis, Macrocystis, and Pterogophora by site x species x depth
Stipe counts across site and year for each species separatey.
9.4 Stipe counts for Nerocystis, Macrocystis, and Pterogophora by site x species at 5 m depth
Just a subset of the plot above.
9.5 Coastwide Stipe counts for Nerocystis, Macrocystis, and Pterogophora
Mean stipe count across sites for the big three algae
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
9.6 Coastwide Stipe counts for Nerocystis, Macrocystis, and Pterogophora at 5 m depth
Same as above but subset out for just 5 m. Mean ignroes 10-m transects.
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
10 INVERTEBRATES
Start with some data manipulations to sum up species by transect.
Table is count of Pycnopodia by year.
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'species', 'zone', 'area', 'taxa'. You can override using the `.groups`
## argument.
## `summarise()` has grouped output by 'year', 'site', 'species', 'zone', 'area'.
## You can override using the `.groups` argument.## # A tibble: 9 × 2
## year no
## <dbl> <dbl>
## 1 2015 0
## 2 2016 4
## 3 2017 0
## 4 2018 3
## 5 2019 1
## 6 2021 0
## 7 2022 0
## 8 2023 1
## 9 2024 110.1 Urchins
10.1.1 Total urchin density - site x year x depth
Total urchins by site by year. total urchins = purple + red + green
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'zone', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone', 'area'. You can
## override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'area', 'zone'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
Max total urchin density at Tatoosh
5-m = 4.0503465 m^-2 10-m = 10.0752194 m^-2
10.1.2 Urchin density by species: year x sites x depth
Moslty at Tatoosh. Mostly purple urchins.
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'zone', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
Max total urchin density at Tatoosh
5-m = 3.7336798 m^-2 10-m = 9.5052194 m^-2
10.1.3 Coastwide urchin density by species
Mean density averaged across sites for a year by species.
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
Purple urchin density in 2015 = 0.0116667 urchins per m^2 Purple urchin density in 2019 = 1.7637508 urchins per m^2
Increase was 151.1786432 fold.
Purple urchin density in 2015 = 0.8145345 urchins per m^2
Density compared to 2015 was 69.8172403 fold.
10.2 Total seastar density: site x year x depth
Sum of all observed seastars. Not species.
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'area', 'zone'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
10.2.1 Coastwide seastars by species
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
10.2.2 An ugly and useless figure
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'zone', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
11 Output data for multivariate statistics
11.1 Combine kelp, fish, and inverts for capscale analysis
Not yet transformed to wide format in order to retain SD & SE info upon saving out. Transform in the Multiariate analysis rmd.
Output data files to use for multivariate ordinations.
Use these files and not raw files. Kelp and invert data have been converted to density to account for different transect lengths etc.
## # A tibble: 468 × 37
## # Groups: site, year, area, zone, transect [468]
## year site transect area zone AUFL BAIT CHNU CITH COTT EMBI GOBI
## <dbl> <fct> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016 Destruc… 1 N 10 0 0 0 0 0 0 0
## 2 2016 Destruc… 2 N 10 0 0 0 0 0 0 0
## 3 2016 Destruc… 3 N 10 0 0 0 0 0 0 0
## 4 2017 Destruc… 1 N 5 0 0 0 0 0 13 0
## 5 2017 Destruc… 4 N 5 0 0 0 0 0 2 0
## 6 2017 Destruc… 1 S 5 0 0 0 0 0 1 0
## 7 2017 Destruc… 2 S 5 0 0 0 0 0 4 0
## 8 2019 Destruc… 1 N 5 0 0 0 0 0 2 0
## 9 2019 Destruc… 2 N 5 0 0 0 0 0 0 0
## 10 2019 Destruc… 3 N 5 0 0 0 0 0 6 0
## # ℹ 458 more rows
## # ℹ 25 more variables: HEXA <dbl>, NO_DATA <dbl>, OPEL <dbl>, PHOL <dbl>,
## # RIMU <dbl>, RYOY <dbl>, SCMA <dbl>, SEBSPP <dbl>, SEBSPPyoy <dbl>,
## # SEBYTyoy <dbl>, SECA <dbl>, SECAyoy <dbl>, SEFL <dbl>, SEMA <dbl>,
## # SEMAyoy <dbl>, SEME <dbl>, SEMY <dbl>, SEMYyoy <dbl>, SENE <dbl>,
## # SENEyoy <dbl>, SEPI <dbl>, SEPIyoy <dbl>, TOTyoy <dbl>, UNID <dbl>,
## # SEBYT <dbl>## # A tibble: 495 × 20
## # Groups: year, site, transect, zone, area [495]
## year site transect area zone AGAFIM ALAMAR COSCOS CYMTRI DESSPP EGRMEN
## <dbl> <fct> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 Neah Bay 1 D 5 0 0 0 0 0 0
## 2 2015 Neah Bay 2 D 5 0 0 0 0 0 0
## 3 2015 Tatoosh… 1 D 5 0 0 0 0 0 0
## 4 2015 Tatoosh… 2 D 5 0 0 0 0 0 0
## 5 2015 Cape Al… 1 D 5 0 0 0 0 0 0
## 6 2015 Cape Al… 2 D 5 0 0 0 0 0 0
## 7 2015 Cape Jo… 1 D 5 0 0 0 0 0 0
## 8 2015 Cape Jo… 2 D 5 0 0 0 0 0 0
## 9 2015 Destruc… 1 D 5 0 0 0.133 0 0 0
## 10 2015 Destruc… 2 D 5 0 0 0.025 0 0.0167 0
## # ℹ 485 more rows
## # ℹ 9 more variables: LAMSET <dbl>, MACPYR <dbl>, NERLUE <dbl>, NO_ALG <dbl>,
## # PLEGAR <dbl>, PTECAL <dbl>, SACGRO <dbl>, SACLAT <dbl>, dPTECAL <dbl>## # A tibble: 497 × 33
## # Groups: year, site, transect, zone, area [497]
## year site transect area zone Pisaster Pycnopodia Stylasterias anemone
## <dbl> <fct> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 Neah Bay 1 D 5 0 0 0 0
## 2 2015 Neah Bay 2 D 5 0 0 0 0
## 3 2015 Tatoosh … 1 D 5 0 0 0 0.00556
## 4 2015 Tatoosh … 2 D 5 0.00556 0 0 0.0111
## 5 2015 Cape Ala… 1 D 5 0.00370 0 0 0
## 6 2015 Cape Ala… 2 D 5 0 0 0 0
## 7 2015 Cape Joh… 1 D 5 0 0 0 0
## 8 2015 Cape Joh… 2 D 5 0 0 0 0.00370
## 9 2015 Destruct… 1 D 5 0 0 0 0
## 10 2015 Destruct… 2 D 5 0.0111 0 0 0.00741
## # ℹ 487 more rows
## # ℹ 24 more variables: barnacle <dbl>, bivalve <dbl>, `blood star` <dbl>,
## # `brood star` <dbl>, chiton <dbl>, crabs <dbl>, cucumber <dbl>,
## # `green urchin` <dbl>, hermit_crabs <dbl>, `kelp crab` <dbl>,
## # `large star` <dbl>, `leather star` <dbl>, `medium star` <dbl>,
## # mobile_invert <dbl>, nudibranch <dbl>, octopus <dbl>,
## # `purple urchin` <dbl>, `red urchin` <dbl>, sea_star_YOY <dbl>, …## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.## # A tibble: 45 × 5
## # Groups: Year [9]
## Year Site Fish Kelp Invertebrates
## <dbl> <fct> <dbl> <dbl> <dbl>
## 1 2015 Cape Johnson 2 2 2
## 2 2015 Cape Alava 2 2 2
## 3 2015 Tatoosh Island 2 2 2
## 4 2015 Neah Bay 2 2 2
## 5 2015 Destruction Island NA 2 2
## 6 2016 Destruction Island 3 7 7
## 7 2016 Cape Johnson 10 13 13
## 8 2016 Cape Alava 12 10 10
## 9 2016 Tatoosh Island 8 9 9
## 10 2016 Neah Bay 10 10 10
## # ℹ 35 more rows11.2 Combine data
combind fish and kelp in wide format using fish COUNT data. include area info for fish and kelp
## `summarise()` has grouped output by 'site', 'year', 'zone', 'area', 'transect'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'site', 'year', 'zone', 'area'. You can
## override using the `.groups` argument.## [1] 123 36## [1] 4656## `summarise()` has grouped output by 'site', 'year', 'area', 'zone'. You can
## override using the `.groups` argument.## [1] 160 20## Joining with `by = join_by(site, year, zone, area)`## [1] 123 5212 Quick view of what species to include in kelp ordinations.
For algae, basically only the big three have any abundance to analyze.
12.1 Algae
## `summarise()` has grouped output by 'species'. You can override using the
## `.groups` argument.
12.2 Fish
Note, the y-axes are very different
## `summarise()` has grouped output by 'taxa'. You can override using the
## `.groups` argument.
12.2.1 Notes
for multivariate analysis; cut and paste to that file
fish.spp = c(“OPEL”, “HEXA”, “SECA”, “SCMA” ,“SENE”, “SEME”, ‘EMBI’,‘GOBI’)
yoy.spp = c(“SECAyoy”, “SEPIyoy”, “SEMYyoy”, “SEBYTyoy”,“RYOY”)
13 SOME USEFUL TABLES
## `summarise()` has grouped output by 'species'. You can override using the
## `.groups` argument.
## Joining with `by = join_by(species)`14 A tibble: 6 × 5
Species Common name size_class Group Total
## Joining with `by = join_by(species)`## # A tibble: 6 × 3
## spp Density SD
## <chr> <dbl> <dbl>
## 1 Pterygophora californica 1.09 1.34
## 2 Macrocystis pyrifera 0.621 2.60
## 3 Nerocystis leutkeana 0.605 1.34
## 4 Laminaria setchellii 0.141 0.431
## 5 Saccharina groenlandica 0.124 0.541
## 6 Pleurophycus gardneri 0.112 0.398## # A tibble: 6 × 4
## Species Taxa Density SD
## <chr> <chr> <dbl> <dbl>
## 1 Balanus nubilis barnacle 0.824 3.28
## 2 Strongylocentrotus purpuratus purple urchin 0.786 2.93
## 3 Nucella lamellosa shelled gastropod 0.197 1.09
## 4 Mesocentrotus franciscanus red urchin 0.140 0.605
## 5 Styela montereyensis tunicate 0.130 0.183
## 6 Cucumaria miniata cucumber 0.107 0.15615 CANOPY KELP FROM DNR
Imports data file from other R file. Plot just kelp canopy. Add to ggarrange below.
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
Mean ha for 2004-2013: 700.6700019 ha +/- 43.6630069 s.e.
Total canopy 2014: 337.2999981 ha
Drop from previous decade: 0.4813964 % of previous cover.
Mean ha for 2015-2020:659.7333332 ha +/- 33.9434596 s.e.
Compared to earlier decade: 0.941575 % of previous cover.
Totals 2014 for Total, Nereo, Macro = 337.2999981, 89.6000009, 247.8999973 ha Totals 2020 for Total, Nereo, Macro = 701.8, 383.4, 318.6 ha
Change was 2.0806404, 4.2790178, 1.2851957 fold.
16 COMBINED FIGURES FOR MS
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
17 Combined Kelp and Temperature figure
17.1 Mean SST
## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
### Kelp
### Kelp and SST combinded figure
